Urbanisation constitutes a major environmental issue of the 21st century. Urban growth causes degradation of natural ecosystems, rapid species loss, acceleration of climate change, and depletion of resources (Groffman et al. 2017, McDonald et al. 2019). For example, the outbreak of COVID-19 is seen as one consequence of the degradation of natural ecosystems, as it leads to shifts in the composition of animal communities, favouring species that are more likely to transmit diseases (Settele et al. 2020). Within the city itself, densification, the decrease of green open spaces, and a continued reliance on ‘grey’ infrastructure approaches, such as engineered solutions with little consideration of natural processes, result in an increasing separation of people from nature and a decreased access to ecosystem services, i.e., the benefits humans derive from nature (IPBES, Brondizio et al. 2019). These include regulation of climatic conditions and mitigation of extreme events such as heavy rainfall (CBO, 2013), but also the positive effects of direct human contact to nature, such as stress reduction, providing a sense of place, and contact to positive microbiota that reduce risks of allergy and diseases (Peccia et al. 2016, Gilbert & Stephens 2018, Marselle et al. 2019). This disconnect of people from nature decreases the liveability of cities and results in reduced human well-being (Maes et al. 2018). Current approaches to make cities more sustainable and liveable fall short in providing breakthrough solutions. While they aim to reduce the impact of cities on nature, they perpetuate the human-nature dichotomy by maintaining the anthropocentric design of urban environments. Consequently, we are far from achieving a biophilic design that fulfils the inherent human need to affiliate with nature in the design of the built environment (Kellert et al. 2008, Beatley 2011, Söderland 2019).

The architecture, engineering, and construction (AEC) industry is undergoing significant changes due to shifting sustainability benchmarks, and technological and methodological advances in computer-aided design and construction. This involves information modelling, data- driven design, computer-aided analysis and simulation, and multi-criteria optimization and evaluation (Zhao et al. 2007, Deutsch 2012, Bier & Knight 2014). Increasing focus is placed on processes such as construction management or life-cycle analyses, and architecture-environment interactions (Dover et al. 2018, Ingrao et al. 2018). However, existing approaches still largely focus on humans and grey infrastructure to manage challenges such as building climate or rainwater management, even if the aim is to integrate green and grey infrastructures in the city (Depietri & McPhearson 2017). Nature is still considered to be an attachment to a building, i.e. green facades or green roofs (Pérez and Perini 2018), rather than being an integral solution on a systemic level. Currently there are no systematic approaches in urban planning and architecture for cohabitation of human and non-human life forms (Franklin 2017). 

In ECOLOPES, we propose a radical change for city development: instead of minimizing the negative impact of urbanisation on nature, we aim at urbanisation to be planned and designed such that nature –including humans– can co-evolve within the city. We envisage a radically new integrated ecosystem approach to architecture that focuses equally on humans, plants, animals, and associated organisms such as microbiota. ECOLOPES will provide the core technology that will help to achieve this vision. In ECOLOPES, we transform the envelope into an ecolope, a multi-species living space for four types of inhabitants: humans, plants, animals, and microbiota (Fig. 1). The regenerative city where each building has an ecolope will be characterized by high biodiversity, which will help to combat the outbreak of diseases. Thus, ECOLOPES develops a new integrative vision of the role of architecture and urban planning for nature-based solutions, which revolutionises perspectives on sustainability.

ECOLOPES will modify the building envelope to enable placement and development of soil, growth and immigration of plants, settlement and reproduction of animals, and the build-up of healthy microbiota, through a systematic approach based on computational design and informed by modelling the relationships between building structure, abiotic environment, plants, animals, humans and microbiota (Fig. 2). Relevant data from biology, soil science, botany, animal ecology, microbiology, as well as human behaviour, will be collected. By modelling soil development, and growth and reproduction of plants and animals, ECOLOPES will explore the way in which architecture can provide for an integrated multi-species habitat and contribute to a healthy ecosystem. 

Building structure and soil are initial design elements. Plants will be planted or they will colonise the ecolope over time, animals as well. These organisms will bring their microbiota. The ecolope is dynamic, allowing for ecological succession as well as human management interventions, so that consequences for successional pathways can be projected. ECOLOPES will simulate the design and development of the ecolope, quantifying the benefits the ecolope has for humans and other organisms. ECOLOPES will also assess the response of humans to different design solutions, which will influence the overall design process of the ecolope. The overall goal of ECOLOPES is to provide the technology that enables an iterative design process based on the simulation of the dynamic development of the ecolope, and of its various subsystems and their interactions (Fig. 3). 

The ECOLOPES system architecture (Fig. 3) includes three main concepts for information (green) and simulation modelling (blue), as well as a validation system (yellow) that proves whether the created computational modelling approach returns valid results. Further, it consists of system components that will work together to implement the overall system. The backend services include data stream analytics, semantic integration, AI and reasoning, while the frontend tools make the computational modelling approach available to the users by providing standard open interfaces (grey).

ECOLOPES will be divided in seven work packages (WPs), to reach five specific objectives (SO):

  • SO1: develop the ECOLOPES computational platform (WP3),
    s basis of the computational design process, including data warehousing capabilities and front-end tools to allow users to view and modify the design outcome;
  • SO2: develop the ECOLOPES Information Model (EIM) Ontology (WP4),
    which defines the fundamental relationships between architecture, the abiotic environment, soil, plants, animals and microbiota. This ontology is the core of the modelling approach to the ecolope ecosystem and central to the design of the ecolope;
  • SO3: develop the computational tools for modelling and visualizing the ecolope (WP5),
    to link the EIM Ontology via a datapoint (Voxel) model to algorithmic processes and tools integrated in Rhino3D and VR (Virtual Reality);
  • SO4: set-up a computational simulation environment (WP6),
    to enable the iterative design process, including computational simulations, multi-criteria analysis and rating strategies, resulting in an informed decision-making process;
  • SO5: demonstrate the effectiveness of the ECOLOPES design platform (WP7),
    by validating the ECOLOPES’ overall design process through specific design cases in four cities, and by assessing synergies of, and trade-offs between, different design solutions.

Description of the individual components

The main components of the ECOLOPES system architecture are (1) the EIM Ontology and its four inhabitant models; (2) the ECOLOPES Algorithms which link the generated datasets from the EIM Ontology to geometry objects; (3) the ECOLOPES data warehouse which provides data to process the ECOLOPES algorithms; (4) the ECOLOPES computational simulation environment which iteratively optimises design outcomes based on KPIs; (5) the ECOLOPES front-end tools that enable to interface with the data-driven recommendation system; and ultimately (6) the ECOLOPES Multi-Species Habitat which evaluates the ECOLOPES from the perspective of all inhabitants (plants, animals, microbiota, and humans).

(1) The EIM Ontology (Knowledgebase) component:

The EIM Ontology is a key component for the data-driven recommendation system. It integrates the following five modelling components into one system by modelling its relationships to index and fuse data to form the basis for the development of the ECOLOPES algorithms and the ECOLOPES computational simulation environment. The generated models are interlinked with established feedback loops. All models refer to the same spatial (site-specific) and temporal (monitoring, assessment time) parameters.

The abiotic environment and architecture model (UNIGE): The model provides analysed datasets from the International and national georeferenced datasets, local building features, normative constraints and design aims and uses (e.g., residential) are adapted to a selected site including its environmental conditions, 3D building geometry and envelope design boundary limits, as well as abiotic parameters for design.

The substrate/soil model (SAAD): The model provides datasets from soils in the areas of the design cases including variables/parameters important for plant growth, carbon sequestration and filtering of pollutants, as well as abiotic measures like texture, pH, volume, water storage capacity, organic carbon and nutrient content, pollution levels.

The plant and vegetation model (SAAD, UNIGE): The model provides analysed and site-specific georeferenced datasets and artificial plant combinations from the building industry and horticultural practice including plant traits, related to resource and abiotic requirements (e.g., N-fixation), life-cycle strategies, and human acceptance (e.g., appearance). PFG dynamics are spatially and temporally modelled as a function of soil, architecture, abiotic conditions, animals, and human management (e.g., mowing, weeding) using an adapted version of the FATE-HD model.

The animal model (SAAD): The model provides analysed datasets from animal presence in and around design cases will be collected from databases (eBird, Ornitho, GBIF, governmental). We focus on birds, mammals, reptiles, amphibian and insects.

The microbiota model (TUM): The model provides analysed site-specific data on microbiota composition in soil, plants and animals from molecular databases like EMBL or NCBI, and own assessments at the design cases using high throughput molecular methods. Most importantly, the microbiota model data will provide variables for catalysts for nutrient/carbon cycling and plant growth promotion to establish the relationships with soil, plants and animals.

The human model (TECHNION): The model provides analysed data on human comfort conditions, physiological, psychological and social benefits of nature to humans, with a focus on various health and well-being and comfort outcomes (including ecosystem services).

(2) The ECOLOPES Algorithms component:

The goal of the component is to create a link between the EIM Ontology models and the computational model in Rhino through a Voxel model. The computational model is a set of algorithmic tools and processes which will run at as a backend service and that will be used by the front-end tools. Based on the design outcome, the toolset will be validated.

(3) The ECOLOPES data warehouse component:

The component stores the analysed data from the EIM Ontology and makes it available to the Algorithms-, computational simulation environment, and front-end tools components. It also stores geometry data which includes the voxel model, generative design outcomes, 3D analysis and simulation results, and metadata.

(4) The ECOLOPES computational simulation environment component:

The component converts the data-integrated computational model (3) into a computational simulation environment by computational simulations (generative design and optimisation), multi-criteria analysis and rating strategies that enable decision-making processes for the design cases (by defining KPIs and interrelationships/hierarchies between them and expert knowledge); and second by validating the computational workflow to ensure integration and interoperability through the design cases in preparation of design validation (envelope and building block evaluation).

(5) The ECOLOPES frontend tools component:

The component enables users to interface with the developed data-driven recommendation system through two frontend tools based on the Rhino platform. Through the tools, the user can access site specific real-time data from the data warehouse and algorithms from the algorithm component to visualise the simulated output of the ontology for a specific period of time, and to apply it to a building design at the selected location. The tool recommends a series of evaluated and optimised design outcomes based on the ECOLOPES to the user, that consider the requirements of all inhabitants equally. Thus, it helps the user in the design decision-making process.

(6) The ECOLOPES Multi-Species Habitat component:

The component provides feedback from real-world design cases at the four different sites to validate the developed data-driven recommendation system from the perspective of all inhabitants (humans: comfort and well-being; plants + animals + microbes: 12 months Building Block analysis, and by comparing the outcomes for all sites). It will provide parameters to the computation simulation environment component for further optimising to achieve the best design outcome.


Beatley, T. (2011). Biophilic Cities: Integrating Nature into Urban Design And Planning. Island Press, Washington, DC.
Bier H & T, Knight. (2014). Data-driven Design to Production and Operation. Footprint 15: 1-8.
Brondizio, E., et al. (2019). Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, IPBES Secretariat. 
CBO, 2013
Depietri, Y. & McPhearson, T. (2017). Integrating the Grey, Green, and Blue in Cities: Nature-Based Solutions for Climate Change Adaptation and Risk Reduction. In Kabisch, N., Korn, H., Stadler, J. & Bonn, A. Nature- Based Solutions to Climate Change Adaptation in Urban Areas. Theory and Practice of Urban Sustainability Transitions. Springer, Cham. pp: 91-109.
Deutsch, R. (2012). Data-driven Design and Construction: 25 Strategies for Capturing, Analysing, and Applying Building Data. Wiley, London.
Dover, J. W. (2018). Introduction to Urban Sustainability Issues: Urban Ecosystem. In Pérez, G. & Perini, K. Nature Based Strategies for Urban and Building Sustainability. Butterworth-Heinemann (Elsevier), Oxford, United Kingdom. pp: 3-15.
Franklin, A. (2017). The more-than-human city. Sociological Review 65(2): 202-217.
Gilbert, J. A. & Stephens, B. (2018). Microbiology of the built environment. Nature Reviews Microbiology 16: 661-670.
Groffman, P. M., et al. (2017). Ecological homogenization of residential macrosystems. Nature Ecology and Evolution 1.
Ingrao, C., et al. (2018). How can life cycle thinking support sustainability of buildings? Investigating life cycle assessment applications for energy efficiency and environmental performance. Journal of Cleaner Production 201: 556–569. 
Kellert, S. R., et al., Eds. (2008). Biophilic Design: The Theory, Science and Practice of Bringing Buildings to Life. Hoboken, New Jersey, USA, John Wiley & sons. 
Maes J, et al. (2018). Mapping and Assessment of Ecosystems and their Services: An analytical framework for ecosystem condition, Publications office of the European Union. 
Marselle, M. R., et al. (2019). Biodiversity and health in the face of climate change. Springer.
McDonald, R. I., et al. (2020). Research gaps in knowledge of the impact of urban growth on biodiversity. Nature Sustainability 3(1): 16-24.
Peccia, J. & Kwan, S. E. (2016). Buildings, beneficial microbes, and health. Trends in Microbiology 24(8): 595- 597.
Pérez, G. & Perini, K. (2018). Nature Based Strategies for Urban and Building Sustainability Elsevier - imprint Butterworth-Heinemann, Oxford, United Kingdom. 
Settele, J., et al. (2020) COVID-19 Stimulus Measures Must Save Lives, Protect Livelihoods, and Safeguard Nature to Reduce the Risk of Future Pandemics. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services 
Söderlund, J. (2019). The emergence of Biophilic design. Switzerland, Springer Nature: 296 pages. 
Zhao H., et al. (2007). Application of data-driven design optimization methodology to a multi-objective design optimization problem. Journal of Engineering Design 18(4): 343-359.